International Public Sector Accounting Standards (IPSAS) Adoption and Implementation in Nigerian Public Sector
Bibliographic record
Abstract
This study evaluated the relationship between IPSAS adoption and financial reporting quality in South West, Nigeria. Specifically, it analysed the effect of IPSAS adoption on credibility and comparability of financial statements. Additionally; salient factors influencing IPSAS implementation were investigated. Primary data collected from one hundred and eighty accountants in South West Nigeria were analysed using tabulation, graphs, factor analysis, and Goodman and Kruskal’s gamma statistics.The empirical results indicated that IPSAS adoption exerted significant and positive relationships with financial reporting quality, credibility and comparability of financial statements. Decisively, discoveries from this study reflect that implementation cost, staff training, technological factor, IPSAS knowledge and awareness and availability of expertise significantly affect IPSAS implementation. However, findings further revealed that IPSAS implementation is not significantly influenced by institutional commitment, cultural, sociological, legal, political and environmental factors. Based on these findings, the authors recommend that considerable amount of money should be set aside for full adoption and implementation of IPSAS in Nigeria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".